Data Science

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Scientists uncover new method to generate protein datasets for training AI

White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

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[2508.07514] Robust MultiSpecies Agricultural Segmentation Across Devices, Seasons, and Sensors Using Hierarchical DINOv2 Models

This article presents a robust segmentation framework using Hierarchical DINOv2 models for reliable plant species and damage identificati...

[2409.04994] Learning nonnegative matrix factorizations from compressed data

This paper presents a framework for scalable nonnegative matrix factorization (NMF) that operates directly on compressed data, allowing f...

[2402.06635] Large and Deep Factor Models

The paper presents a deep neural network model for constructing stochastic discount factors in finance, highlighting a new component call...

[2312.11797] Data-Driven Merton's Strategies via Policy Randomization

This paper explores Merton's expected utility maximization problem in incomplete markets, introducing a data-driven approach using policy...

[2507.19234] Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV

The paper introduces Virne, a benchmarking framework designed for Reinforcement Learning-based resource allocation in Network Function Vi...

[2307.14397] A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot

This survey explores generative modeling under constraints of limited data, few shots, and zero shots, presenting challenges and methodol...

[2306.14297] Inference for relative sparsity

The paper discusses a novel approach to inference for relative sparsity in healthcare decision-making, addressing the need for uncertaint...

[2103.14203] Deep Two-Way Matrix Reordering for Relational Data Analysis

The paper presents Deep Two-Way Matrix Reordering (DeepTMR), a novel method for matrix reordering that utilizes neural networks to extrac...

[2507.14186] A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction

This paper presents a novel framework for predicting low-altitude network coverage using disentangled representation learning, addressing...

[2507.04704] SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

The paper introduces SPATIA, a novel multimodal model for predicting spatial cell phenotypes by integrating cellular morphology, gene exp...

[2602.11712] Potential-energy gating for robust state estimation in bistable stochastic systems

This article presents a novel method called potential-energy gating for robust state estimation in bistable stochastic systems, enhancing...

[2506.20430] An Agentic System for Rare Disease Diagnosis with Traceable Reasoning

The paper presents DeepRare, a multi-agent system utilizing large language models for the differential diagnosis of rare diseases, demons...

[2602.10603] dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning

The paper presents dnaHNet, a novel tokenizer-free autoregressive model designed for genomic sequence learning, achieving significant eff...

[2602.09238] Feature salience -- not task-informativeness -- drives machine learning model explanations

This paper investigates the factors influencing feature importance in machine learning model explanations, emphasizing that feature salie...

[2602.08655] From Robotics to Sepsis Treatment: Offline RL via Geometric Pessimism

This article presents a novel framework called Geometric Pessimism for Offline Reinforcement Learning (RL), enhancing performance in robo...

[2602.08535] Causal Schrödinger Bridges: Constrained Optimal Transport on Structural Manifolds

The paper introduces the Causal Schrödinger Bridge (CSB) framework, enhancing generative modeling by addressing challenges in causal infe...

[2506.08672] RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling

RuleReasoner introduces a novel approach to rule-based reasoning using domain-aware dynamic sampling, enhancing reinforcement learning fo...

[2602.05319] Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective

This paper introduces Accelerated Sequential Flow Matching, a Bayesian filtering framework that enhances real-time inference in stochasti...

[2602.05119] Unbiased Single-Queried Gradient for Combinatorial Objective

This paper presents a novel stochastic gradient method for combinatorial optimization that requires only a single query, enhancing effici...

[2602.02929] RPG-AE: Neuro-Symbolic Graph Autoencoders with Rare Pattern Mining for Provenance-Based Anomaly Detection

This paper presents RPG-AE, a neuro-symbolic framework combining Graph Autoencoders and rare pattern mining for detecting Advanced Persis...